


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[Any] 			=		'deta'
			_UpperCamelCase		:       Optional[Any] 			=		{
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			}




			def __init__(				self   ,    snake_case=None   ,    snake_case=900   ,    snake_case=2048   ,    snake_case=6   ,    snake_case=2048   ,    snake_case=8   ,    snake_case=6   ,    snake_case=1024   ,    snake_case=8   ,    snake_case=0.0   ,    snake_case=True   ,    snake_case="relu"   ,    snake_case=256   ,    snake_case=0.1   ,    snake_case=0.0   ,    snake_case=0.0   ,    snake_case=0.02   ,    snake_case=1.0   ,    snake_case=True   ,    snake_case=False   ,    snake_case="sine"   ,    snake_case=5   ,    snake_case=4   ,    snake_case=4   ,    snake_case=True   ,    snake_case=300   ,    snake_case=True   ,    snake_case=True   ,    snake_case=1   ,    snake_case=5   ,    snake_case=2   ,    snake_case=1   ,    snake_case=1   ,    snake_case=5   ,    snake_case=2   ,    snake_case=0.1   ,    snake_case=0.25   ,    **snake_case   ,    ):

						'''simple docstring'''


						if backbone_config is None:
									logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."  )
									UpperCamelCase__       =							CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]  )
						else:
									if isinstance(snake_case   ,    snake_case  ):
												UpperCamelCase__       =							backbone_config.pop("model_type"  )
												UpperCamelCase__       =							CONFIG_MAPPING[backbone_model_type]
												UpperCamelCase__       =							config_class.from_dict(snake_case  )

						UpperCamelCase__       =							backbone_config
						UpperCamelCase__       =							num_queries
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							d_model
						UpperCamelCase__       =							encoder_ffn_dim
						UpperCamelCase__       =							encoder_layers
						UpperCamelCase__       =							encoder_attention_heads
						UpperCamelCase__       =							decoder_ffn_dim
						UpperCamelCase__       =							decoder_layers
						UpperCamelCase__       =							decoder_attention_heads
						UpperCamelCase__       =							dropout
						UpperCamelCase__       =							attention_dropout
						UpperCamelCase__       =							activation_dropout
						UpperCamelCase__       =							activation_function
						UpperCamelCase__       =							init_std
						UpperCamelCase__       =							init_xavier_std
						UpperCamelCase__       =							encoder_layerdrop
						UpperCamelCase__       =							auxiliary_loss
						UpperCamelCase__       =							position_embedding_type
						# deformable attributes
						UpperCamelCase__       =							num_feature_levels
						UpperCamelCase__       =							encoder_n_points
						UpperCamelCase__       =							decoder_n_points
						UpperCamelCase__       =							two_stage
						UpperCamelCase__       =							two_stage_num_proposals
						UpperCamelCase__       =							with_box_refine
						UpperCamelCase__       =							assign_first_stage
						if two_stage is True and with_box_refine is False:
									raise ValueError("If two_stage is True, with_box_refine must be True."  )
						# Hungarian matcher
						UpperCamelCase__       =							class_cost
						UpperCamelCase__       =							bbox_cost
						UpperCamelCase__       =							giou_cost
						# Loss coefficients
						UpperCamelCase__       =							mask_loss_coefficient
						UpperCamelCase__       =							dice_loss_coefficient
						UpperCamelCase__       =							bbox_loss_coefficient
						UpperCamelCase__       =							giou_loss_coefficient
						UpperCamelCase__       =							eos_coefficient
						UpperCamelCase__       =							focal_alpha
						super().__init__(is_encoder_decoder=snake_case   ,    **snake_case  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.encoder_attention_heads




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.d_model







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							copy.deepcopy(self.__dict__  )
						UpperCamelCase__       =							self.backbone_config.to_dict()
						UpperCamelCase__       =							self.__class__.model_type
						return output






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		CustomTokenizer
			pass


